Abstract
The phase-matching quantum key distribution (PM-QKD) protocol has been widely researched since it was proposed. In this paper, the performance of asymmetric PM-QKD protocol is discussed and the efforts of statistical fluctuation and source error on asymmetric PM-QKD protocol are analyzed through numerical simulations. In the case of limited data sets, system parameters need to be optimized to increase the key rate. However, traditional exhaustive traversal or local search algorithms cannot meet the time requirement of real-time communication. With the development of machine learning, using machine learning for parameter optimization has been widely applied in various disciplines. This paper uses recurrent neural network (RNN) to predict the optimization parameters of asymmetric PM-QKD. The results show that RNN can quickly and accurately predict optimization parameters, which can provide a reference for future real-time QKD networks.
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All data generated and analyzed during this study is available from the corresponding author on reasonable request.
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Acknowledgements
This work is supported by Fund of State Key Laboratory of Information Photonics and Optical Communications (Beijing University of Posts and Telecommunications) (IPOC2023ZT05), P. R. China
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Wang, L., Dong, Q. & Jiao, R. Parameter optimization in decoy-state phase-matching quantum key distribution. Quantum Inf Process 22, 373 (2023). https://doi.org/10.1007/s11128-023-04130-x
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DOI: https://doi.org/10.1007/s11128-023-04130-x